Voluntary Movement I. Psychophysical principles & Neural control of reaching and grasping

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Voluntary Movement I.

Psychophysical principles

& Neural control of reaching and grasping

Claude Ghez, M.D.

Introduction

• Reflex and voluntary movements are sensorimotor transformations.

– Feedforward vs. feedback control.

• Reflex control differs from voluntary control

– Spatial organization of reflexes reflects hard-wired connections vs. behavioral demands and context: decisions. Voluntary movements are organized to be appropriate to address behavioral goal: kinematic vs. dynamic transformations - internal models.

– Reflex latency & duration reflect mainly fixed neuromuscular constraints: conduction, transmission, contraction. These, also influence timing of voluntary movement, but information processing and accuracy constraints are the critical reaction time and movement duration.

– Neural organization of voluntary movements is highly dependent on learning and plasticity. Adaptability is critical over long term.

Today

1. Voluntary movements require decisions and information processing: Latency and duration:

• Reaction time.

• Parallel processing.

• Speed-accuracy tradeoffs.

2. Sensorimotor transformations in reaching and grasping.

• Kinematics: visuomotor transformations. Movement vectors

• Dynamics: internal models.

• Role of vision and proprioception in feedback and feedforward control

3. Organization of motor cortical areas for reaching and grasping

• Multiple motor areas

• Somatotopic organization

• Redundancy

Latency or “Reaction time” depends on decision

Reaction time paradigm

• Warning -> cue= go signal

•Simple RT: single or predictable cue Subject knows what response to make in advance

•Choice: multiple unpredictable cues (e.g. colors, symbols, spatial locations) each requiring different responses.

Stage theory of reaction time

“Reaction time” depends on practice and learning

Unpredictable stimuli

Predictable sequence

Response features can be processed in parallel

Synch Cue

Tones

Elbow force

Measured by

Strain gauge

Target

Resting force

>20

0

100-

200

High

Middle

Low

Low

Middle

High

<100

High

Middle

Right direction

Low

Low

Middle

Wrong

High direction

SpeedAccuracy tradeoff (Fitts’ law)

Error varies with speed

Reaching reflects several sensorimotor transformations kinematic and dynamic planning

Reaching: hand and joint kinematics are planned independently

Extent and direction are planned in advance

Paths are straight Hand trajectories:

Speed & acceleration

Scale with distance

Reaching involves scaling a ‘trajectory primitive’ to target distance.

Learned scaling factors

& reference frame

Accuracy requires knowledge of mechanical properties of the limbs (“the plant”): role of proprioception

Normal control Patient without proprioception

Mobility

Extent

Directional variations in inertial resistance are corrected by compensatory variations in movement time

Proprioception is critical

Control MFG Patient MA

PK Acceleration

300

Movement time (ms)

0

Peak

Acceleration

Movement Direction

900

0

Peak

Acceleration

Movement Direction

Proprioceptive information is used for feedforward control: Internal models

Reaching reflects several sensorimotor transformations

Kinematics and dynamics

Parallel planning of concurrent actions: hand preshaping

Motor areas of the brain

Macaque Monkey Human

Corpus Callosum

Premotor cortex

Supplementary motor area (SMA)

Primary motor cortex

Supplementary motor area (SMA)

Primary motor cortex

Central sulcus

Premotor cortex

Primary sensory cortex

Posterior Parietal

Cortex

Prefrontal cortex

Somatotopic organization revieled by electrical stimulation of the cortical surface

Electrical stimulation is medically useful

Early experiments

Electrical stimulation is medically useful:

More recent experiments

Representation of individual muscles is patchy and distributed

Deltoid Ext. Carpi Rad.

Posterior

Anterior

Medial

Posterior

Lateral

Anterior

Medial Lateral

Cell activity associated with movement of individual digits is broadly distributed: convergence of spinal projections

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